Batched Gaussian Process Bandit Optimization via Determinantal Point Processes
Batched Gaussian Process Bandit Optimization via Determinantal Point Processes
复制标题
通过行列式点过程进行批量高斯过程老虎机优化
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Pushmeet Kohli
中科院分区：
文献类型：
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作者：
Tarun Kathuria;A. Deshpande;Pushmeet Kohli
高斯过程bandit优化已经成为优化噪声黑盒函数的一个强大工具。机器学习中的一个示例是超参数优化，其中目标函数的每次评估可能需要训练模型，这可能涉及数天甚至数周的计算。这种所谓的“贝叶斯优化”的大多数方法只允许对参数空间进行顺序探索。然而，通常希望提出批量或参数值集以同时探索，特别是当我们可以使用大型并行处理设施时。批处理方法需要对批处理中不同评估之间的交互进行建模，这在复杂的场景中可能是昂贵的。在本文中，我们提出了一种新的方法，并行贝叶斯优化建模的多样性，一批通过确定性点过程（DPP）的内核是自动学习。这使我们能够推广以前的结果，以及证明更好的后悔界限的基础上，DPP采样。我们对各种合成和真实世界的机器人和超参数优化任务的实验表明，我们基于DPP的方法，特别是基于DPP采样的方法，优于最先进的方法。
Gaussian Process bandit optimization has emerged as a powerful tool for optimizing noisy black box functions. One example in machine learning is hyper-parameter optimization where each evaluation of the target function may require training a model which may involve days or even weeks of computation. Most methods for this so-called "Bayesian optimization" only allow sequential exploration of the parameter space. However, it is often desirable to propose batches or sets of parameter values to explore simultaneously, especially when there are large parallel processing facilities at our disposal. Batch methods require modeling the interaction between the different evaluations in the batch, which can be expensive in complex scenarios. In this paper, we propose a new approach for parallelizing Bayesian optimization by modeling the diversity of a batch via Determinantal point processes (DPPs) whose kernels are learned automatically. This allows us to generalize a previous result as well as prove better regret bounds based on DPP sampling. Our experiments on a variety of synthetic and real-world robotics and hyper-parameter optimization tasks indicate that our DPP-based methods, especially those based on DPP sampling, outperform state-of-the-art methods.